Long-term Planning of Generation,
Transmission and Distribution Assets
Joydeep Mitra
Electrical & Computer Engineering
Michigan State University
East Lansing, MI 48824
(517) 353-8528
mitraj@msu.edu
Outline of today’s presentation
• Introduction
• Planning for a sustainable energy system—a
top-down approach
– Sustainable generation, delivery and utilization
– Cyber-enabled system
• Planning approaches
– Experience with distribution system expansion
planning
– Bulk power system expansion
– Return of integrated system planning
• Summary of research and other experience
• Concluding remarks
Mitra: education and experience
• Education
– Ph.D. in Electrical Engineering, 1997, Texas A&M
University, College Station, TX
– B.Tech.(Hons.) in Electrical Engineering, 1989,
Indian Institute of Technology, Kharagpur, India
• Experience
– Nine years in academia
• Assoc. Prof., Michigan State University, 2008-present
• Assoc. Prof., New Mexico State University, 2003-08
• Asst. Prof., North Dakota State University, 2000-03
– Five years in industry and consulting
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Research projects—past and ongoing
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Autonomous control of microgrids (NSF Collaborative, 2007-10)
Protection of microgrids and smart distribution systems
CAREER Award on microgrid architecture (NSF, 2002-07)
Resource optimization in microgrids (SNL, 2005-07)
Identification of modes of catastrophic failures of power systems
Advanced transformer modeling (BPA, 2002-03)
Distributed generation in demand management (OTP, 2001-03)
Role of Induction Motors in Stability (OTP, 2001-02)
Dynamic Rating of Transmission Components (OTP, 2001-02)
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Other contributions
• Fundamental contributions to reliability analysis
– A direct method for determination of failure frequency
indices using state space decomposition
– Method of pruning and simulation
– State space decomposition with linearized flow
representation
• Experimental and hardware development
– Three-phase transformer modeling (BPA sponsored)
– Synchronization hardware for off-the-shelf standby
generators (US Patent 7,180,210)
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Educational experience
• Nine years in academia
– Taught 15 different lecture and laboratory courses
– Advised 17 graduate students—6 current, 11 graduated
– Mentoring 1 post-doctoral research associate
• Short Courses
– Power system reliability
– Power system fundamentals
– Life extension of substations
• IEEE Tutorial
– Electric delivery system reliability tutorial offered at three
IEEE conferences earned TC recognition award
• IPU courses for regulators and policy makers
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Service and outreach activities
• Student activities
– Chair of IEEE-PES Student Meetings SC 2007-08
– In six years (2003-08) as SC officer, I helped organize eight
Student Programs, with a total participation of over 770
students (20% women, 15% minorities)
– Eight poster contests with over 360 participants
• Other IEEE activities
– Current chair of Reliability, Risk and Probability
Applications SC
– Participation in standards development
– Involvement in several committees, SCs, WGs and TFs
• University service: served on numerous committees
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Other leadership activities
• Associate Director, Electric Utility Management
Program, NM State University, 2003-08
– Industry liaison
– Educational fund-raising
• Conferences organized
– TCPC for Power System Analysis, Computing and Economics
committee at IEEE-PES General Meeting 2009
– Chair, North American Power Symposium 2007
– Co-Chair, Distributed and Renewable Energy Symposium 2003
• Conference sessions organized/chaired
– Organized and chaired/co-chaired two panel sessions
– Chaired eight technical paper sessions
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Contribution to technology roadmaps
• NSF-NIST National Workshop on Research Directions for Future Cyber-Physical
Energy Systems, Baltimore, MD, June 3-4, 2009.
• “Smart Grids” breakout session facilitator at Great Lakes Alliance for
Sustainable Energy Research Workshop, Chicago, IL, May 26, 2009.
• NSF Workshop on the Future Power Engineering Workforce, Washington, DC,
November 29-30, 2007.
• Workshop on Power System Security, sponsored by Indian Ministry of Power,
Kharagpur, India, January 13-14, 2006.
• NSF-EPRI Workshop on Understanding and Preventing Cascading Failures,
Denver, CO, October 27-28, 2005.
• DOE Workshop on National Electric Delivery Technologies Roadmap,
Washington, DC, July 8-9, 2003.
• NSF/EPRI/DOE Workshop on Future Research Directions for Complex
Interactive Electric Networks, Washington, DC, November 16-17, 2000.
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Energy in the 21st century and beyond
• The 20th century has seen significant advances in energy
generation, delivery and utilization, but has also
produced tremendous impact on the environment and
natural resources.
• Significant changes must be made to how we generate,
deliver and use energy so as to
– establish sustainable utilization, and
– restore environmental balance.
• Education must occur at all levels:
– researchers;
– workforce;
– consumers.
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Need to recompose energy portfolio
• Decrease fossil fuel consumption
– 85% of today’s energy supply comes from fossil fuels
– Transportation and electric generation need to move away from fossil fuels
– Fossil fuels are the predominant contributors to environmental pollution
(COx, SOx, NOx, particulates)
– Will also lead to energy independence
• Increase renewable generation
– 7% of today’s energy supply comes from renewable sources (hydroelectric,
geothermal, wind, solar, biomass)
– Renewable generation must increase significantly but responsibly
• Increase nuclear generation suitably
– 8% of today’s energy supply comes from nuclear power
– Nuclear generation must increase so that there is adequate supply from
steady sources
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Planning for sustainable generation
• Technological enablers
– Solar generation technologies (photovoltaic and solar thermal)
– Wind generation and integration
– Other generation technologies: geothermal, biomass/biofuels,
tidal, kinetic, wave, ocean thermal
– Storage technologies
• Old technology, new role: nuclear power
• Need for holistic analyses
– Life-cycle and cost-benefit, including decommissioning
– Environmental impact during manufacture, during useful life
and after decommissioning
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Sustainable and secure delivery
• Technological enablers
– “Smart” transmission grids: synchrophasors, wide-area
measurement and control, FACTS, dynamic rating, data
management optimization
– “Smart” distribution systems: smart meters and communication, distribution automation, microgrids, V2G interface
– Advanced stability, control, security, protection, optimization
– Market design and operation
• Re-emergence of integrated resource planning
– Transmission additions and upgrades have become
increasingly expensive and time consuming
– Transmission expansion should not be decoupled from
generation
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Sustainable utilization
• Technological enablers
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Energy efficient buildings with thermal storage
“Smart” homes and “smart” appliances
Demand response and load management programs
Energy efficient transportation: hybrid and electric vehicles
Storage and direct conversion technologies
• Growing need for conservation
• Demand profiles will change significantly
– Composition of load is changing
– Load factor is likely to change too
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The “smart” or cyber-enabled system
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Benefits:
• Enables active participation by
consumers
• Optimizes asset utilization and
efficient operation
• Anticipates and responds to
system disturbances
• Accommodates all generation
and storage options
• Provides power quality for the
digital economy
• Enables new products,
services and markets
Challenges:
• Data management
• Interoperability
• Cybersecurity
Grid resilience
• Transient and dynamic stability
• Reliability (service continuity)
• Security (resistance to disruption, from both inadvertent
and malicious causes)
• Strategic and tactical countermeasures
– Systemic vulnerabilities will have to be addressed through
appropriate integrated resource planning
– Tactical responses will have to be programmed into the
“cyber” layer
– Distributed energy resources (DER) will have a role as a backup
system
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Experience with distribution
system expansion planning:
Microgrid architecture
• Grand challenge:
Transformation of today’s distribution systems into
the modern, reliable, secure, robust, autonomous,
self-organizing, self-healing, intelligent power
delivery systems of tomorrow.
• Based on DoE’s National Electric Delivery
Technologies Roadmap
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What are Microgrids?
‘Under this vision, integrated clusters of small
(<200kW) DERs provide firm power with a
guaranteed level of power quality through
operation in either grid-connected or island modes.’
(U. S. Department of Energy, “Transmission Reliability Multi-Year
Program Plan FY2001–2005,” July 2001.)
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Distributed energy resources
• Generating Devices
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Windmills
PV and solar thermal
Microturbines
Fuel cells
Biomass and biofuels
Geothermal power
Tidal and ocean thermal
Reciprocating engines
• Storage Devices
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(U. S. Department of Energy,
“Transmission Reliability Multi-Year
Program Plan FY2001–2005,” July 2001.)
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Batteries
Ultracapacitors
SMES
Flywheels
• Combined heat and power
• Interruptible loads
Autonomous microgrid
Control
Protection
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Two layers of microgrid architecture
• Reliability-centered optimal expansion strategies
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Optimal network expansion
Optimal resource deployment
Integrated expansion problem
Supported by NSF and SNL
• Control and protection
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Multi-agent systems (MAS) for autonomous control
Communication-assisted protection
Optimized distributed sensing strategies
Supported by NSF
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Reliability-centered optimal system
expansion: motivation
• Reliability (service continuity) and security
(resistance to disruption) are major
concerns driving microgrid development
• Reliability-differentiated services will be
offered in the future
• It makes sense to use reliability as a
criterion in planning for system expansion
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Reliability-driven expansion
• Given:
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A distribution system with an anticipated load growth
DG installation options: sizes of DG (distributed
generation) clusters, possible locations, deployment
costs
Network augmentation options: rights of way, cost of
installing new feeders
• Determine:
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Least cost deployment and network expansion
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System-wide and locational reliability guarantees (EIR
or Availability)
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Study system (for augmentation)
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System data
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Modeling challenges
Simultaneously include in the optimization
framework the dependencies between line
characteristics and performance
• Dependencies in characteristics:
between capacity and impedance
between impedance and length
between length and cost
between capacity and cost
• Dependencies in operation:
KVL and KCL
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Solution approach
Accommodation of dependencies in
characteristics:
Unit link concept
Accommodation of KVL and KCL:
Linear approximation of power dispatch
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Unit link concept
A unit-link connecting a given pair of buses is a line of
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fixed capacity
fixed cost per unit length
fixed impedance per unit length
length corresponding to the right of way between the buses
Unit-links between different bus pairs will have different
lengths, impedances and costs, but same capacity.
A link between a given pair of buses can consist of one or more
(an integral number of) unit-links connected in parallel
between that bus pair. The cost of a link is equal to the total
cost of the unit links that constitute the link plus a fixed cost
for installing the link along the corresponding right of way.
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Linear flow representation
(Dispatch module)
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Solution strategy 1:
Particle Swarm Optimization
PSO imitates the behavior of a flock of birds (swarm of particles)
in search of food (objective function).
Velocity vector:
Position vector:
Comments:
• Collective intelligence of swarm helps identify objective
• Parameters need to be tweaked to achieve good performance
(speed and convergence)
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PSO implementation: modeling
• Search space is (NG+NT)-dimensional; each
point in this space corresponds to a
configuration for system augmentation
• Search for a point in this space that minimizes
expansion cost (J=JG+JT) subject to systemwide and locational reliability criteria
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Reliability corresponding to a particle
• The existing system is augmented by the coordinates
of the particle
• The augmented system is evaluated using a
contingency selection procedure
 All first order transmission and first and second order
generation contingencies are considered
 For each contingency the dispatch module is solved and
system and locational indices are determined
 Each overall system or locational index is determined from
the weighted sum
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PSO implementation: constraints
• Particles assume positions with non-negative
coordinates
• Penalty for violating reliability stipulations:
problem is modified to
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Solution steps
1. Build search space, initialize particles in feasible region
2. For each particle, determine reliability indices and expansion
cost
3. Using cost as objective (or “fitness”) function (lower cost
implies better fitness), determine the particle velocity
vectors
4. Using the particle velocity vectors, update the particle
position vectors
5. Repeat steps 2 through 4 until the cost function converges
(standard error less than tolerance)
6. Augment system by coordinates of solution point (group best
at convergence)
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Result: augmented system
Solution strategy 2:
Dynamic Programming
Consider generator locations to be fixed; then
Objective function is network
expansion cost, subject to
system-wide and locational
reliability criteria:
Inclusion of generators
described later
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Solution method using DP
• Solved in a stage-wise manner using Dynamic
Programming (DP).
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In each stage the network is incremented by one unit-link.
DP stage: represents the number of unit-links utilized
DP state: reliability vector
DP decision: the unit-link to be added at each stage
• Reliability vector for each unit link addition is
determined using contingency evaluation method
As done for each particle, described before
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Challenges with DP method
Overwhelming computational complexity
• At each stage, all configurations with same
reliability were examined and only lowest cost
configuration was retained, all others were
discarded
• Algorithm was parallelized and distributed
over a 128-node Beowulf cluster
• Solved in two phases because of
parallelization
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Comparison of PSO and DP
PSO Solution
Min augmentation cost: 486
DP Solution
Min augmentation cost: 460
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Grid security research
• Problem statement: Identify likely event sequences that lead
to catastrophic failures
• Challenges:
– Model system dependencies
– Identify most likely even sequences
• Approach: layered genetic algorithm
Line 3
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G1
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Line 1
Line 7
Line 4
Line 2
Line 5
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G2
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Line 6
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Return of integrated resource planning
• Tomorrow’s sustainable, resilient system can
be accomplished by thoughtful and responsible
integrated resource planning
• Expansion planning methods described before
can be adapted to bulk system with suitable
modifications
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Select most resilient solution from multiple optima
Include determination of appropriate resource mix
Include ac flow and appropriate load models
High performance computing will play a key role
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Concluding remarks
• Creative approaches will be critical to the
development of planning tools for the
complex energy system of the future
• Multiple, and sometimes conflicting,
objectives will contribute to the complexity
• Presence of variable resources will require
geospatial modeling and weather
data/forecasts
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